Why Sigma Computing? | Sigma Computing

Why Sigma

Sigma is a cloud analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real time.

Built for the cloud

Data never leaves CDW
Secure, governed, up to date
Any cloud data warehouse, any cloud

Instantly familiar to users

Get users creating value immediately
Extensive training not required
Maximize productivity and ROI faster

Unique scale + performance

Billions of rows in seconds
Maximize your CDW investment
Performs against very large datasets, automatically

There are lots of analytics vendors. Is Sigma all that different?

Absolutely. Legacy vendors in analytics have been in the market for a long time—decades, in many cases. They have great features, but they’ve all struggled mightily with the rapid customer adoption of the cloud. Additionally, data volumes that used to be on-premise (and somewhat manageable) are now in the cloud—and have become massive. Think: billions of rows and columns.

Legacy vendors have large amounts of technical debt, and may also have to support on-premise implementations. Adding a cloud option to their portfolio means refactoring their architecture to address how the cloud works. This is unmanageable, and we’ve seen this play out in the market with vendor consolidations, acquisitions, and other struggles to support customer requirements.

From day one, Sigma was built for the cloud as well as massive data scale with a familiar interface. We take full advantage of modern cloud-design principles and data warehouses to deliver scalable performance—even in billion-row use cases.

Customers of all sizes have adopted Sigma for these and many more reasons.

How is Sigma different from the user perspective?

Legacy analytics platforms worked fine when the data was small, and only limited insights from rolled-up data was needed. Analysis was usually done by people with deep analytical expertise. But then the cloud data warehouse entered the scene. The modern cloud data warehouse became popular in the mid to late 2010s. Cloud-based data warehousing solutions started to gain traction as companies increasingly realized the benefits of moving their data storage and processing to the cloud.

This changed how analytics needed to process data.
Today’s business users expect to be able to use all of the data in the cloud data warehouse for decision-making themselves.
They expect to be able to drill into the smallest detail. They also expect to be able to adapt to changing business needs quickly, without rebuilding everything, and without re-writing a bunch of code.
They expect to work with the data in a familiar interface (i.e.: a spreadsheet). They don’t want to learn another tool to do their job.
They don’t have time to wait for a development cycle to get the information they need to make a decision.
Sigma is built for today’s needs and users. With Sigma's intuitive spreadsheet-like interface, users can analyze, visualize, and collaborate—all at the scale and speed of business demands.

Other competitors?

Yep, there are many others and the stories are all very similar. Lots of functionality, proprietary learning required, not built ground-up for the cloud, and don’t perform well at scale. They use extracts of data, trading granularity for performance. In Sigma, you get both.

Sigma vs. Looker

Sigma vs. Tableau

Sigma vs. Power BI

Sigma vs. ThoughtSpot

How does Sigma support its customers?

In a word, Sigma support is fanatical. So much so, support is directly in the product, including live chat with a Sigma support associate. Sigma customers enjoy working with us rather than sitting frustrated and waiting for a response.

Is Sigma innovating?

For sure!

One example is Input Tables, which are really unique and powerful for business users who want to do their own ad-hoc scenario modeling of existing data with values they provide, outside of existing data.

When data isn't in the warehouse, it usually requires a cumbersome technical and people process to ETL data into the warehouse. Now users who need to add data to the warehouse are able to do so directly.

Sigma customers already use Input Tables for:

Manual data entry of key values
Analytic Modeling

Scenarios
Forecasts
Territory Planning
Sales Planning
Supply Chain Modeling

For example, a Sales Director may want to see the effect of changing Connecticut from the New England territory to the Mid-Atlantic. Input Tables allows them to make that adjustment and see how it could change forecasted revenue by observing the BEFORE and AFTER bar charts.

It’s important to understand that this does not change the data in the warehouse. Input Table data is always persisted in the warehouse separately, and no data is ever held by Sigma.

Real time support in minutes, not days

The support team is nailing it, it's really good support. Please keep it and even if you make it "VIP Treatment," then we want to be VIP. We love your live support.

Nan Xiao, CTO, Greenland Capital Management

There hasn't been a single data question I haven't been able to answer in Sigma once I engage the support team for help.

Mathew Jennings, Director, Orennia

What really helped make my life easier in making the case for Sigma was the thought, effort, and responsiveness that your team put in whereas the competition simply lacked that level of depth!

Orr Sergev, Director of Analytics, OpenWeb